CRISP-DM

Along with the many topics at Predictive Analytics World SF this week representing the leading edge of analytic sophistication, there were also presenters speaking to the blocking and tackling necessary to nurture analytic thinking and scale adoption within their organization. Since many organizations are facing these same analytic adoption and scale problems, I’d like to highlight […]

Many data science projects use the popular and well established CRISP-DM methodology. However, CRISP-DM has limitations especially regarding business understanding and deployment. The decision modeling process and the graphical decision requirements diagram addresses these challenges. CRISP-DM Popular, but with Limitations Gregory Piatetsky of KDnuggets writes following the KDnuggets Data Mining Methodology Poll: “CRISP-DM remains the […]

We have been helping several organizations improve their analytic and data science projects. Like many users of analytics, these organizations find that their analytic teams often lack a clear understanding of the business problem, resulting in projects that lose their way or produce analytic models that don’t get operationalized, deployed or used. We have helped […]

Join me and Tina Owenmark of Cisco when we speak on The Role of Decision Modeling in Creating Data Science Excellence at Predictive Analytics World in San Francisco. Cisco’s Data Science Office focuses not just on data science, but also on shaping the questions and answers for Cisco’s operational groups. They focus not on technology or […]

The biggest challenge facing organizations adopting analytics is closing the gap between business value and analytics results. This is becoming increasingly serious as more organizations make investments in data mining, predictive analytics, data science, machine learning and all forms of analytics. Ensuring that these investments in analytics and analytic technology show a return means understanding how […]

As I discussed in my earlier post, analytics or data science teams know that two key challenges for analytics projects are making sure you solve the real business problem (framing the problem) and making sure you can operationalize the result (deployment). In this second post I am going to talk about deployment.

Experienced analytics or data science teams know that two key challenges for analytics projects are making sure you solve the real business problem (framing the problem) and making sure you can operationalize the result(deployment). In this post I am going to talk about framing the problem and I’ll follow-up with another on deployment.

One of the best ways to think about framing is to consider the questions the analytic team should get answers to before they start building a model:

The webinar has taken place. You can view the recording here. Leading organizations today are looking to scale their advanced analytics capabilities, especially data mining and predictive analytics, to improve business performance, reduce fraud and improve customer responsiveness. However traditional analytic project approaches are hard to scale and difficult to implement in the real-time environment […]

The presentation and recording from our recent webinar, The Value of Predictive Analytics and Decision Modeling, are now available. Successful predictive analytic projects follow a well-defined approach from requirements to modeling, implementation and deployment, embedding the analytic results in operational systems that improve business performance. In this live webinar recording, James Taylor, CEO and Principal Consultant […]

The value proposition of predictive analytics is almost always to improve decision-making. Being explicit about the decision-making to be improved is an effective tool for framing analytic requirements. Download our new white paper, Framing Analytic Requirements, to how learn to achieve analytical, data-driven decisions with decision modeling and the Decision Model and Notation (DMN) standard. […]